Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.
Evaluation of Semi-supervised Learning for Classification of Protein Crystallization Imagery.
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DOI：
10.1109/secon.2014.6950649
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发表时间：
2014-03
期刊：
影响因子：
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通讯作者：
Aygün RS
中科院分区：
文献类型：
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作者：
Sigdel M;Dinç İ;Dinç S;Sigdel MS;Pusey ML;Aygün RS
在本文中，我们研究了两种包装方法的性能，半监督学习算法分类的蛋白质结晶图像与有限的标记图像。首先，我们使用朴素贝叶斯（NB）和顺序最小优化（SMO）作为基本分类器的自训练来评估半监督方法的性能。这些分类器返回的置信度值用于选择高置信度预测以用于自训练。其次，我们使用NB，SMO，多层感知器（MLP），J48和随机森林（RF）分类器分析了另一个两阶段思想（YATSI）半监督学习的性能。将这些结果与使用相同训练集的基本监督学习进行比较。我们在由2250个蛋白质结晶图像组成的数据集上进行实验，这些图像用于不同比例的训练和测试数据。我们的研究结果表明，NB和SMO使用自训练和YATSI半监督方法提高了监督学习的准确性。另一方面，MLP，J48和RF使用基本的监督学习表现更好。总的来说，随机森林分类器在我们的数据集的监督学习中产生了最好的准确性。
In this paper, we investigate the performance of two wrapper methods for semi-supervised learning algorithms for classification of protein crystallization images with limited labeled images. Firstly, we evaluate the performance of semi-supervised approach using self-training with naïve Bayesian (NB) and sequential minimum optimization (SMO) as the base classifiers. The confidence values returned by these classifiers are used to select high confident predictions to be used for self-training. Secondly, we analyze the performance of Yet Another Two Stage Idea (YATSI) semi-supervised learning using NB, SMO, multilayer perceptron (MLP), J48 and random forest (RF) classifiers. These results are compared with the basic supervised learning using the same training sets. We perform our experiments on a dataset consisting of 2250 protein crystallization images for different proportions of training and test data. Our results indicate that NB and SMO using both self-training and YATSI semi-supervised approaches improve accuracies with respect to supervised learning. On the other hand, MLP, J48 and RF perform better using basic supervised learning. Overall, random forest classifier yields the best accuracy with supervised learning for our dataset.